Research On Reactive Power Optimization Based On Improved Genetic Algorithm

Reactive power optimization could improve voltage quality and reduce power loss.And it has an importance significance to power system security and economic operation.Reactive power optimization essentially is a nonlinear mathematical programming problem.It contains continuous variables and discrete variables.The traditional algorithm requires that the target function is continuous and conductive.whether it convergence is closely related to the initial value.With the development of artificial intelligence algorithms,Especially genetic algorithms,it exhibit unique advantages in dealing with reactive power optimization.But there are also some shortcomings just as early maturity,slow convergence,falling into local solutions,etc.Based on the current domestic and foreign research,combined with the advantages and disadvantages of various algorithms,this paper propose a genetic algorithm base on the hybrid Newton method.At the same time,aiming at the characteristics of reactive power optimization of power system.In order to improve the efficiency of the algorithm,the paper improve simple genetic algorithm in fitness function,encoding,crossover and mutation operators.So Genetic Algorithm exert its benefits in reactive power optimization completely.To make the power system reactive power optimization more scientific,this paper establish a mathematical model that makes the minimum power loss as objective function.Simulation results show that: compared with simple Genetic Algorithm,Genetic Algorithm base on the hybrid Newton method and Improved Genetic Algorithm have better ability of global convergence and convergence rate in IEEE-14 and IEEE-30.